A valuable method for extrapolating the detailed voltage shape of EEG records, during those times when they are obscured by large transients such as those arising from scalp muscles, seems to be autoregression. This method develops a linear functional to be applied to an uncorrupted EEG segment, which predicts with relatively small error, the future voltage values for some period of time. The period during which errors are acceptably small depends on the character of the preceding EEG record, and has not been fully investigated as yet. Computer programs for developing that functional, applying it, and comparing its predictions with actual EEG values have been completed and tested during the reporting period. For well-developed normal alpha waves, the period of good preditability varies (in a way not yet fully understood) from 0.1 seconds up to 0.4 seconds. This is useful for some limited subset of practical muscle- interference compensation, but we hope to do better by further development of related techniques. One such improvement will be to combine, in a minimum-error fashion, the predictions from preceding time periods, and the retrodictions from succeeding unperturbed data periods. This should at least double the duration of good compensation capability, and may in some cases do substantially more. The other major improvement proposed for the coming year is to adapt the method of Kalman filtering to this problem; by making a more flexible adaptation to possible non- stationarities in EEG data, this method should yield predictions having lower error in general, hence a longer period of acceptably small error.